In 2025, 18% of companies based in France used artificial intelligence. Retail stops at 13%. Behind the query artificial intelligence ecommerce sit a dozen different AI promises: product search, recommendation, pricing, customer support and marketing. This guide sorts those families by purchase profile and by the state of your data. It helps you decide which solution to deploy first on your ecommerce website and how to improve the conversion rate.
Artificial intelligence in ecommerce: definition, use families and benefits
What is artificial intelligence applied to ecommerce?
L’Artificial intelligence applied to online retail covers the technologies that read product data and customer information in order to recommend, forecast or decide in place of a fixed rule. A machine learning model learns from shopper behavior on your site and adjusts the customer experience in real time: search, price, stock, customer support. The main use cases cover product recommendations, demand forecasting, fraud detection and content creation. Natural language processing and deep learning read the text and the images of your product pages. An AI agent or assistant goes further: it holds an exchange with the customer and calls the right tools to answer. These solutions serve marketing, support, logistics, pricing and conversion from the same product information. Data becomes the fuel of a data driven digital commerce.
Where do French companies stand?
According to Insee, 18% of companies based in France with 10 employees or more used at least one AI technology in 2025 (source: Insee Première no. 2120, July 2026, 2025 data). That is 8 points more in one year. Retail reaches only 13%, six points below the European average for the sector. Its usage rate has still been multiplied by 1.3 in twelve months. Among the companies that use AI, 80% buy an off-the-shelf piece of software or system rather than build it. The real question for an ecommerce leadership team is therefore no longer whether AI is needed. It is the order of the projects and the quality of the data that feeds them. A team that analyses its own data before buying can avoid a pilot that leads nowhere.
What benefits should you expect from AI in ecommerce?
Four benefits come back on the sites that succeed, provided the data is ready:
- Conversion rate : a shopper who finds the right product faster buys more often.
- Team productivity : repetitive support, content and marketing tasks leave the calendar.
- Customer satisfaction : an accurate answer at any hour improves the consumer experience.
- Cost control: fewer stockouts, less fraud and fewer avoidable returns.
None of these gains is automatic: to improve a result you first have to measure where it starts. The classic trap is buying the tool before measuring the indicator it is supposed to move. Compare each project against a numbered baseline, because without one the revenue growth credited to AI remains a belief. Serious optimization is also judged over time rather than over one promotional week.
Which risks should you watch: data, bias and transparency?
Three risks come back. The first concerns customer and product data: missing or contradictory attributes lead an agent to invent an answer. The second lies in algorithmic bias : an engine trained on history reproduces what sold yesterday and hides what would suit the shopper. The third is transparency : telling a customer they are talking to an AI is becoming a legal obligation, detailed further down. The privacy of personal information also weighs on every project. Each company must document how its teams correct these gaps. In practice the real difficulty sits almost always in the data rather than in the model.
Product search and guided discovery
A classic search engine needs the right keywords. With AI, the online store understands a vague request such as “a gift for someone who loves hiking” and asks questions to narrow it down. This is the ground of product discovery : the shopper is guided towards the right item, including from a photo thanks to visual search . Our page on AI-powered search details the architecture. Data required: complete and consistent product attributes. Indicator: the share of searches with no result on the site.
Recommendation tuned to the shopper
Product recommendations read visit behavior through browsing history to personalize the storefront. They also feed email campaigns and abandoned cart recovery. Their limit is known. A “similar products” engine serves low-basket fashion but fails on a holiday, a property or a vehicle. The shopper first has to clarify the need. To go further, see AI product recommendation and recommendation engines. Data required: history and attributes. Indicator: revenue per visit and the conversion rate.
Product content and data quality
Generative AI writes a product description in seconds, enriches attributes and translates pages into several languages. This task weighs heavily in the workflow of a catalog team. The time saved is real on a large product catalog and it improves the consistency of product pages. So is the risk: without a reliable reference base, AI produces wrong pages at scale. Sound product information management therefore comes before any product content creation, for the website as much as for the marketplaces. Data required: a governed reference base. Indicator: the share of complete and validated pages. This task also serves organic search.
Dynamic pricing
Dynamic pricing adjusts prices according to demand, stock and the market. It suits fast-moving catalogs. It suits high-consideration purchases poorly: a shopper who compares several offers over weeks reacts badly to a price that moves from one day to the next. The classic trap remains price inconsistency across channels. Data required: reliable stock and market prices that are legally accessible. Indicator: margin per order.
Demand forecasting, stock and logistics
Demand forecasting relies on sales history to anticipate peaks. It feeds inventory management , reduces stockouts and makes order fulfillment easier to optimize across the whole supply chain. Analysing past sales remains the starting point. The gains weigh above all on high-volume order management and on the logistics function of the company. Data required: clean and complete sales history. Indicator: the stockout rate. A poor history produces a wrong forecast with a lot of confidence. Optimizing stock on false figures can only make stockouts worse.
Fraud detection and payment security
Fraud detection scores every transaction and spots anomalies in real time through machine learning. The trade-off sits in false positives : a system that is too strict blocks real customers and costs more than the fraud it avoids. Data required: a labelled transaction history. Indicator: the false positive rate.
Assistants and customer support
AI answers questions about order status, returns and delivery terms, then hands over to a human advisor. That handover happens when the question falls outside its scope. These tools lighten customer support and improve customer service outside opening hours through an immediate answer. The resolution rate without human intervention measures how useful they are. A support chatbot is enough when most tickets concern order tracking and after-sales returns. Our guide dedicated to ecommerce chatbots details the use cases and the guardrails.
Support for the sales advisor
In sales where a human closes the deal, AI acts as a sales copilot : it summarizes the customer history, prepares the meeting and drafts the follow-up. The AI shopping assistant on the buyer side and the copilot on the advisor side share the same data. They shorten the customer journey without stripping the human from it and they free up time for selling. The table below sums up the eight families: the diagnostic that follows is built on it.
Which AI for which type of purchase?
Why does the type of purchase decide the use case?
Three purchase profiles call for three different uses. The impulse purchase, on a small basket, benefits from recommendation and pricing. The considered purchase calls for comparisons and quality search. The high-consideration purchase, such as a holiday, a home or a vehicle, demands a dialogue: the shopper does not always know what they want before being asked. The sectors concerned have understood this: in real estate, AI serves marketing and sales first, at companies that account for 48% of the sector’s workforce (source: Insee Première no. 2120, July 2026, 2025 data). A filter or a similar-products engine does not replace that dialogue. It takes an AI that reasons over the customer journey of high-consideration purchases and that knows how to personalize the conversation.
The five-question diagnostic
Lists of use cases do not tell you where to start. Answer five questions about your catalog, your shoppers and your data. The diagnostic returns the three AI families to prioritize, the data each one depends on and the indicator to track. The rules are qualitative: no percentage gain is promised.
These three families set the order of the projects rather than a certainty. Read them again with the figures from your own business.
How should you read the diagnostic result?
The result is a starting point rather than a verdict. It often puts a data family first, such as product content or guided discovery, rather than a showcase tool. The first priority of a complex catalog is to make the information reliable before handing it to an agent. The question “what is the best AI for ecommerce” therefore has no general answer. It depends on the purchase profile, the state of the data and the dominant channel. Run the diagnostic again each quarter with the figures from your dashboards to follow how priorities move. Each team can also measure the gap between its strategy and its means there, factually.
Point solution, suite or platform: how do you choose?
Three options exist. The point solution solves one precise problem such as fraud detection or a support chatbot. The suite from a commerce vendor covers several families inside the same ecosystem. The platform orchestrates AI agents across several journeys from a common data layer. In France, 80% of the companies that use AI buy off-the-shelf software (source: Insee Première no. 2120, July 2026, 2025 data). Building in-house remains open to companies with scarce data science and engineering skills, provided they can sustain it over time.
Choose according to the purchase profile the diagnostic identified. A point solution is enough for fraud or stock. A high-consideration journey calls for a platform, because the AI has to reason over several sources at once. That integration of sources is what separates an end-to-end solution from an isolated module.
AI assistants already send shoppers to retail sites
What Adobe measured over the 2025 holiday season
AI tools no longer just answer: they send shoppers to retail sites. Over the 2025 holiday season in the United States, Adobe measured a rise of 693.4% in visits coming from generative AI tools compared with the previous year. These are clicks by humans who started from an assistant and landed on a site, not bots (source: Adobe Analytics, January 2026). Here is what that measurement shows.
Beyond the rise in volume, these visitors convert 31% more than other sources and bounce less. The scope is American, so the figure does not transpose directly to the French market. It does point to a direction that ecommerce leadership teams can no longer ignore: market trends are also playing out away from the site.
Making a product catalog readable by an AI agent
An AI agent does not read a page the way a human does. It queries structured data through protocols such as MCP (Model Context Protocol) or UCP (Universal Commerce Protocol). Three conditions apply: complete product data attributes, an up-to-date price and availability and written return policies. An incomplete catalog stays invisible to the agent or ends up badly cited. Kleio opens its customers’ catalogs to assistants such as ChatGPT and Gemini through these protocols. For the detailed mechanics, see the agentic commerce protocols and our analysis of ChatGPT Shopping.
What the AI Act requires from online retailers
L’Article 50 of the European AI regulation (Regulation (EU) 2024/1689) requires that people be informed when they interact with an AI system. That transparency obligation, which covers any interaction with the public, has applied since 2 August 2026 (source: Regulation (EU) 2024/1689, articles 50 and 113). For a retail site it concerns any assistant that talks to a customer: the interface has to say so clearly. Failure to comply carries a fine of up to 15 M€ or 3% of worldwide annual turnover, whichever is higher (source: article 99 of the regulation). This requirement sits in phase 3 of the roadmap for any company that deploys an assistant, alongside access rights and logging.
A data-first roadmap in 4 phases
Phase 1: audit product data, documents and customer memory
The diagram above sums up the four phases. The first consists of auditing three stores before any deployment:
- Product data and its attributes: dimensions, materials, compatibilities, prices and availability.
- Reference documents: terms of sale, guides and technical sheets.
- Customer memory: history of interactions and known preferences.
The exit criterion comes down to one question: is a decisive attribute missing on more than one product in five? If so, it is a no-go: complete the data before going further. This threshold is a decision marker to adjust to your own catalog rather than a market statistic.
Phase 2: pilot a single journey with a single indicator
Pick one journey and one indicator from the diagnostic in order to optimize a single lever. Example: guided discovery on a category where shoppers hesitate, measured by the conversion rate. Measure shopper behavior before and after on a comparable sample, then decide. Deliverable: a pilot report. The go / no-go criterion is direct: has the indicator moved on that journey? A pilot centred on a single journey reads without ambiguity whereas a diffuse pilot gets discussed for months. That discipline protects the investment decision and informs the strategy.
Phase 3: govern the agents and compliance
Set the rules before scaling up. Each agent receives access rights limited to its function: what it may read or write. Answers outside the approved data are blocked. Every exchange is kept through logging for audit. The customer is told they are talking to an AI, in line with article 50. Deliverable: a register of rights and controls. Go / no-go: can an agent answer outside the approved data? If so, the phase is not finished.
Phase 4: scale across every channel
Extend the validated journey to the site, the store, the advisor and external AI assistants. Deliverable: a deployment plan per channel. Go / no-go criterion: does the data stay consistent from one channel to the next? A price or an availability that differs between the site and an external agent ruins trust within a few exchanges. At this stage, a single data and agent platform avoids rebuilding the same connectors for every channel. The next section shows how Kleio handles that point.
Kleio: agentic AI for complex purchase journeys
A platform for high-consideration purchases
Kleio builds an Agentic Commerce platform for complex, high-value sales. The journeys it addresses are first of all high-consideration purchases: travel, real estate, automotive and hospitality, then wholesale and manufacturing. Three surfaces of use coexist: the product discovery journey on the customer side, the sales advisor’s tool and the exposure of the catalog to external AI assistants through MCP and UCP. This platform makes it possible to personalize the conversation and it belongs to agentic commerce, where an agent leads the buyer from discovery to decision.
Unified data before the agents
Kleio’s principle is to solve the data problem before deploying agents. The platform gathers product data, reference documents and customer memory into three purpose-built stores, with a separate governance layer. The AI agents answer only from approved data. That architecture matches phase 1 of the roadmap: a reliable product reference base and reliable documents before any conversation with a customer. Kleio integrates with existing CRMs rather than replacing them: the sales team keeps its tools and its working habits.
The Orpi case: 1.250 agencies, 8.000 advisors
Orpi, a real estate network, deployed the Kleio platform on orpi.com and across 1.250 agencies, serving 8.000 advisors. Its CEO, Guillaume Martinaud, reports that the platform was live in three months. His account describes a search able to understand the customer’s real need:
“The real estate journey of tomorrow will be increasingly AI-assisted, and we chose to take the lead. Our clients expect a fluid search experience capable of understanding their real needs as if they were speaking to a human. In three months, we kept our commitment: the platform is live, on Orpi.com and in each of our 1.250 agencies. It is a disruptive tool that leverages the immense richness of our data to better serve our clients and our 8.000 advisors, and positions Orpi where future buyers now make their first searches.” - Guillaume Martinaud, CEO of Orpi
Sectors covered and security requirements
Kleio has pre-built business ontologies for seven sectors : automotive, energy and utilities, insurance, manufacturing, real estate, travel and hospitality, wholesale. On security, the platform is SOC 2 and CCPA compliant. It isolates data per customer and applies rights per operation. It pseudonymizes sensitive fields before any call to an external model. To see these mechanisms at work on your own product catalog, request a demo.
